pytorch报RuntimeError: mat1 and m

https://www.kaggle.com/ 编译代码

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Sequential(torch.nn.Conv2d(1 , 10, 5), torch.nn.ReLU(), torch.nn.MaxPool2d(2))
        self.conv2 = torch.nn.Sequential(torch.nn.Conv2d(10, 20, 5), torch.nn.ReLU(), torch.nn.MaxPool2d(2))
        self.conv3 = torch.nn.Sequential(torch.nn.Conv2d(20, 40, 3), torch.nn.ReLU(), torch.nn.MaxPool2d(2))
        self.dense = torch.nn.Sequential(torch.nn.Linear(40, 128), torch.nn.ReLU(), torch.nn.Linear(128, 10))

    def forward(self, x):
        conv1_out = self.conv1(x)
        conv2_out = self.conv2(conv1_out)
        conv3_out = self.conv3(conv2_out)
        res = conv3_out.view(conv3_out.size(0), -1)
        print('out', conv3_out.shape)  //torch.Size([64, 40, 3, 3])
        out = self.dense(res)
        
        return F.log_softmax(out, dim=1)

运行代码报错：RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x360 and 40x128)
原因：这种问题源于所定义的最后一层池化层输出的形状和全连接层输入的形状不一样。我们通过在前向传播函数中打印池化层的输出形状可知：

 print('out', conv3_out.shape)  //torch.Size([64, 40, 3, 3])

([64, 40, 3, 3]) 其中的64是设置的batch_size，后三维才是其真正的形状，而全连接层的输入是一维特征，因此需要添加一个flatten层进行压平操作。压平后如下：

[64, 40*3*3]

上面代码dense改为：

 self.dense = torch.nn.Sequential(torch.nn.Linear(360, 128), torch.nn.ReLU(), torch.nn.Linear(128, 10))

代码不在报错

原文章：https://blog.csdn.net/Learn_signal/article/details/126722129